TEAM
Pawsitive Retrieval 2
Afsin Ozdemir, Enhao Feng, Ness Mayker
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Retrieval Augmented Generation (RAG) is a framework of enhancing the output of generative models by providing external knowledge during generation. In the setting of question answering, RAG provides a large language model with a set of documents related to the specific query, allowing the model to generate answer that is more accurate and comprehensive.
In this project, we build a RAG pipeline that takes in a query from user, retrieves relevant information from the database, and output a summarization by a large language model. More specifically, we focus on building a model that can retrieve the information fast (sub-seconds) and can also filter out irrelevant information after retrieval. We also provide a framework using RAGAs to evaluate our model.
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![](https://static.wixstatic.com/media/a994932411404ef3bb797ba005125f5d.png/v1/fill/w_45,h_45,al_c,q_85,usm_0.66_1.00_0.01,blur_3,enc_auto/a994932411404ef3bb797ba005125f5d.png)